MODE (for Machine-learning Optimized Design of Experiments) is a collaboration of physicists and computer scientists who target the use of differentiable programming in design optimization of detectors for particle physics applications, extending from fundamental research at accelerators, in space, and in nuclear physics and neutrino facilities, to industrial applications employing the technology of radiation detection.
We aim to develop a modular, customizable, and scalable, fully differentiable pipeline for the end-to-end optimization of articulated objective functions that model in full the true goals of experimental particle physics endeavours, to ensure optimal detector performance, analysis potential, and cost-effectiveness.
The main goal of our activities is to develop an architecture that can be adapted to the above use cases but will also be customizable to any other experimental endeavour employing particle detection at its core. We welcome suggestions, as well as interest in joining our effort, by researchers focusing on use cases for which this technology can be of benefit.
The above program has been submitted in a concise form as an expression of interest
to the JENAA group
At INFN and Università of Padova Dr. Tommaso Dorigo
, Dr. Pablo De Castro Manzano
, Dr. Federica Fanzago
, Dr. Lukas Layer
, Dr. Giles Strong
, Dr. Mia Tosi
, and Dr. Hevjin Yarar
At Université catholique de Louvain Dr. Andrea Giammanco
, Prof. Christophe Delaere
, Mr. Maxime Lagrange
, and Dr. Pietro Vischia
At Université Clermont Auvergne, Prof. Julien Donini
, and Mr. Federico Nardi
(joint with Universitá di Padova)
At the Higher School of Economics of Moscow, Prof. Andrey Ustyuzhanin
, Dr. Alexey Boldyrev
, Dr. Denis Derkach
, and Dr. Fedor Ratnikov
At the Instituto de Física de Cantabria, Dr. Pablo Martínez Ruíz del Árbol
At CERN, Dr. Jan Kieseler
At University of Oxford Dr. Atilim Gunes Baydin
At New York University Prof. Kyle Cranmer
At Université de Liège Prof. Gilles Louppe
At GSI/FAIR Dr. Anastasios Belias
At Rutgers University Dr. Claudius Krause
At Uppsala Universitet Prof. Christian Glaser
At TU-München, Prof. Lukas Heinrich
and Mr. Max Lamparth
At Durham University Dr. Patrick Stowell
At Lebanese University Prof. Haitham Zaraket
The Scientific Coordinator of the MODE Collaboration is Dr. Tommaso Dorigo
, INFN-Sezione di Padova
The Steering Board of the MODE Collaboration includes:
- Prof. Julien Donini, UCA
- Dr. Tommaso Dorigo, INFN-PD
- Dr. Andrea Giammanco, UCLouvain
- Dr. Fedor Ratnikov, HSE
- Dr. Pietro Vischia, UCLouvain
Below is a list of events organized by the MODE Collaboration:
Below is a list of publications by the MODE Collaboration:
- The MODE Collaboration, "Toward Machine Learning Optimization of Experimental Design", published in Nuclear Physics News International in March 2021 (submitted December 2020). Available in the journal website or as a preprint here and on INSPIRE
Below is a concise list of relevant publications to the research interests of the MODE Collaboration. MODE members among the authors are indicated in boldface:
- T. Dorigo, A. Giammanco, P. Vischia (editors) et al., "Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper", arXiv:2203.13818.
- N. Simpson, L. Heinrich, "neos: End-to-End-Optimised Summary Statistics for High Energy Physics", arXiv:2203.05570
- T. Dorigo, S. Guglielmini, J. Kieseler, L. Layer, G.C. Strong, "Deep Regression of Muon Energy with a K-Nearest Neighbor Algorithm", arXiv:2203.02841.
- L. Heinrich, M. Kagan, "Differentiable Matrix Elements with MadJax", arXiv:2203.00057
- J. Kieseler, G.C. Strong, F. Chiandotto, T. Dorigo, L. Layer, "Calorimetric Measurement of Multi-TeV Muons via Deep Regression" Eur. Phys. J. C (2022) 82: 79, doi:10.1140/epjc/s10052-022-09993-5
- C. Neubüser, Jan Kieseler, Paul Lujan, "Optimising longitudinal and lateral calorimeter granularity for software compensation in hadronic showers using deep neural networks" Eur. Phys. J. C (2022) 82: 92 (2022) doi:10.1140/epjc/s10052-022-10031-7
- A. Gunes Baydin, B.A. Pearlmutter, A.A. Radul, and J.M. Siskind, "Automatic Differentiation in Machine Learning: a Survey", Journal of Machine Learning Research (JMLR) 18 (153) (2018) 1, http://jmlr.org/papers/v18/17-468.html
- T. Dorigo, "Geometry Optimization of a Muon-Electron Scattering Detector," Physics Open 4 (2020) 100022, arXiv:200200973[physics.ins-det], doi:10.1016/j.physo.2020.100022.
- T. Dorigo, J. Kieseler, L. Layer and G. Strong, "Muon Energy Measurement from Radiative Losses in a Calorimeter for a Collider Detector", arXiv:2008.10958 [physics.ins-det] (2020).
- S. Shirobokov, A. Ustyuzhanin, A. Gunes Badyin et al., "Differentiating the Black-Box: Optimization with Local Generative Surrogates", arXiv:2002.04632v1 [cs.LG] (2020).
- K. Cranmer, J. Brehmer, and G. Louppe, "The frontier of simulation-based inference", arXiv:1911.01429[stat.ML] (2019), Proceedings of the National Academy of Sciences.
- F. Ratnikov, "Using machine learning to speed up and improve calorimeter R&D", JINST 15 (2020) C05032, doi: 10.1088/1748-0221/15/05/C05032.
- F. Ratnikov, D. Derkach, A. Boldyrev, A. Shevelev, P. Fakanov, L. Matyushin, "Using machine learning to speed up new and upgrade detector studies: a calorimeter case", to appear in proceedings of CHEP 2019, https://arxiv.org/abs/2003.05118
- A. Boldyrev, D. Derkach, F. Ratnikov, A. Shevelev, "ML-assisted versatile approach to Calorimeter R&D", arXiv:2005.07700
- P. Giubilato et al., "iMPACT: innovative pCT scanner", IEEE Nucl. Science Symposium and Medical Imaging Conference (NSS/MIC) IEEE (2015), https://ieeexplore.ieee.org/abstract/document/7581240.
- S. Wuyckens, A. Giammanco, P. Demin, and E. Cortina Gil, "A Portable muon telescope based on small and gas-tight Resistive Plate Chambers"<, Phil. Trans. Royal Soc. A377 (2019) 2137, arXiv:1806.06602v2[physics.ins-det] (2018), doi:10.1098/rsta.2018.0139.
- J. Kieseler, "Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data", arXiv:2002.03605[physics.data-an] (2020).
- P. de Castro Manzano and T. Dorigo, "INFERNO: Inference-Aware Neural Optimization", Comp. Phys. Commun. 244 (2019) 170; Arxiv:1806.04743v2 [stat.ml] (2018), doi:10.1016/j.cpc.2019.06.007 .
- J. Brehmer, K. Cranmer et al., "MadMiner: Machine learning-based inference for particle physics", Comput. Softw. Big Sci. 4 (2020) 1, 3, doi:10.1007/s41781-020-0035-2.
- G. Louppe, J. Hermans, and K. Cranmer, "Adversarial Variational Optimization of Non-Differentiable Simulators", PMLR 89:1438-1447, 2019, arXiv:1707.07113[stat.ML].
- K. Cranmer, J. Pavez, and G. Louppe, "Approximating Likelihood Ratios with Calibrated Discriminative Classifiers", arXiv:1506.02169[stat.ML] (2015).